## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.0
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## Warning: package 'ggplot2' was built under R version 3.6.2
## Warning: package 'tibble' was built under R version 3.6.2
## Warning: package 'tidyr' was built under R version 3.6.2
## Warning: package 'purrr' was built under R version 3.6.2
## Warning: package 'dplyr' was built under R version 3.6.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
##
## set_names
## The following object is masked from 'package:tidyr':
##
## extract
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following object is masked from 'package:purrr':
##
## compact
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## The following object is masked from 'package:purrr':
##
## transpose
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
## Loading required package: nlme
## Warning: package 'nlme' was built under R version 3.6.2
##
## Attaching package: 'nlme'
## The following object is masked from 'package:dplyr':
##
## collapse
## This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
## Loading required package: emmeans
## Warning: package 'emmeans' was built under R version 3.6.2
## The 'lsmeans' package is now basically a front end for 'emmeans'.
## Users are encouraged to switch the rest of the way.
## See help('transition') for more information, including how to
## convert old 'lsmeans' objects and scripts to work with 'emmeans'.
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
## Warning: package 'multcomp' was built under R version 3.6.2
## Loading required package: mvtnorm
## Warning: package 'mvtnorm' was built under R version 3.6.2
## Loading required package: survival
## Warning: package 'survival' was built under R version 3.6.2
## Loading required package: TH.data
## Loading required package: MASS
## Warning: package 'MASS' was built under R version 3.6.2
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
##
## Attaching package: 'TH.data'
## The following object is masked from 'package:MASS':
##
## geyser
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## Parsed with column specification:
## cols(
## .default = col_double(),
## group = col_character(),
## measure = col_character()
## )
## See spec(...) for full column specifications.
## [1] 1029 103
## Parsed with column specification:
## cols(
## sub = col_double(),
## FC = col_double(),
## group = col_character()
## )
## Parsed with column specification:
## cols(
## sub = col_double(),
## FC = col_double(),
## group = col_character()
## )
## Parsed with column specification:
## cols(
## sub = col_double(),
## FC = col_double(),
## group = col_character()
## )
## [1] 343 3
## Joining by: sub, group
## Joining by: sub, group
## [1] 1029 118
## Joining by: sub
## [1] 1029 120
## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## 'data.frame': 1029 obs. of 15 variables:
## $ IC_98 : num 0.00412 0.00362 0.00481 0.0027 0.00449 ...
## $ IC_99 : num 0.00377 0.00454 0.00343 0.00579 0.00428 ...
## $ group : Factor w/ 3 levels "no","ob","ov": 1 1 1 1 1 1 1 1 1 1 ...
## $ measure : Factor w/ 3 levels "centrality","cluster",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ FC : num 0.134 0.123 0.151 0.16 0.105 ...
## $ Age_in_Yrs : int 22 29 35 27 26 34 33 25 33 31 ...
## $ ZygosityGT : Factor w/ 0 levels: NA NA NA NA NA NA NA NA NA NA ...
## $ Race : Factor w/ 6 levels "Am. Indian/Alaskan Nat.",..: 6 3 6 6 6 6 6 3 2 6 ...
## $ Ethnicity : Factor w/ 3 levels "Hispanic/Latino",..: 2 2 1 2 2 2 2 2 2 2 ...
## $ BMI : num 21.1 22.2 20.4 22.6 24.5 ...
## $ HbA1C : num NA 5.4 5.3 5.4 4.7 4.7 5.5 NA NA NA ...
## $ Hypothyroidism : int NA NA 0 NA NA 0 0 0 0 NA ...
## $ Hyperthyroidism : int NA NA 0 NA NA 0 0 0 0 NA ...
## $ OtherEndocrn_Prob: int NA NA 0 NA NA 0 0 0 0 NA ...
## $ Mother_ID : Factor w/ 339 levels "50263","50371",..: 140 186 141 231 60 278 236 192 262 15 ...
## [1] "" "Amygdala" "Auditory"
## [4] "BrainStem" "Caudate" "Cerebellum"
## [7] "CinguloOperc" "Default" "DorsalAttn"
## [10] "FrontoParietal" "MedialParietal" "None"
## [13] "Putamen" "Smhand" "Smmouth"
## [16] "Thalamus" "VentralAttn" "VentralDiencephalon"
## [19] "Visual"
## The following `from` values were not present in `x`: cerebellum
## [1] "no" "ov" "ob"
## [1] "White"
## [2] "Black or African American"
## [3] "Asian"
## [4] "Multi-racial"
## [5] "Unknown or not reported and Hispanic"
## [6] "Multi-racial and Hispanic"
## [7] "Black or African American and Hispanic"
## [8] "White and Hispanic"
## contrast estimate SE df t.ratio p.value
## noVov 0.134 0.488 340 0.275 0.9898
## noVob -1.019 0.542 340 -1.880 0.1719
## ovVob -1.153 0.580 340 -1.990 0.1356
##
## P value adjustment: sidak method for 3 tests
##
## Descriptive statistics by group
## group: no
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 156 28.33 4.11 27.5 28.23 5.19 22 36 14 0.2 -1.27 0.33
## ------------------------------------------------------------
## group: ov
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 109 28.19 3.72 28 28.16 4.45 22 35 13 0.02 -1.06 0.36
## ------------------------------------------------------------
## group: ob
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 78 29.35 3.75 30 29.48 4.45 22 35 13 -0.23 -1.1 0.43
## contrast estimate SE df t.ratio p.value
## noVov -5.01 0.280 340 -17.874 <.0001
## noVob -12.03 0.311 340 -38.653 <.0001
## ovVob -7.02 0.333 340 -21.097 <.0001
##
## P value adjustment: sidak method for 3 tests
##
## Descriptive statistics by group
## group: no
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 156 22.2 1.69 22.38 22.25 1.93 18.11 24.95 6.84 -0.28 -0.86 0.14
## ------------------------------------------------------------
## group: ov
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 109 27.2 1.56 27.06 27.15 2.05 25 29.98 4.98 0.21 -1.3 0.15
## ------------------------------------------------------------
## group: ob
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 78 34.23 3.62 33.39 33.76 2.79 30.1 43.94 13.84 1.08 0.29 0.41
#HBA1c
## contrast estimate SE df t.ratio p.value
## noVov -0.0178 0.0645 224 -0.276 0.9897
## noVob -0.1779 0.0745 224 -2.388 0.0524
## ovVob -0.1601 0.0786 224 -2.037 0.1230
##
## P value adjustment: sidak method for 3 tests
## Warning: Removed 116 rows containing non-finite values (stat_ydensity).
## Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
## 'x' values
##
## Descriptive statistics by group
## group: no
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 103 5.21 0.36 5.2 5.23 0.3 3.12 6 2.88 -1.83 9.27 0.04
## ------------------------------------------------------------
## group: ov
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 76 5.23 0.24 5.2 5.23 0.3 4.8 5.9 1.1 0.17 -0.29 0.03
## ------------------------------------------------------------
## group: ob
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 48 5.39 0.7 5.3 5.33 0.3 4.5 9.6 5.1 4.34 23.93 0.1
##
## Pearson's Chi-squared test
##
## data: table(dat$group, dat$Gender)
## X-squared = 9.4234, df = 2, p-value = 0.008989
## $F
## no ov ob
## 90 42 39
##
## $M
## no ov ob
## 66 67 39
## Warning in chisq.test(table(dat$group, dat$race_eth)): Chi-squared approximation
## may be incorrect
##
## Pearson's Chi-squared test
##
## data: table(dat$group, dat$race_eth)
## X-squared = 22.324, df = 14, p-value = 0.07219
## $White
## no ov ob
## 94 72 51
##
## $`Black or African American`
## no ov ob
## 22 17 14
##
## $Asian
## no ov ob
## 19 6 0
##
## $`Multi-racial`
## no ov ob
## 4 3 1
##
## $`Unknown or not reported and Hispanic`
## no ov ob
## 0 2 4
##
## $`Multi-racial and Hispanic`
## no ov ob
## 2 1 1
##
## $`Black or African American and Hispanic`
## no ov ob
## 1 0 0
##
## $`White and Hispanic`
## no ov ob
## 14 7 7
fit_function<-function(y,data){
fit<-lm(y~group+Age_c+HBA1c_c+Gender+race_eth+FC, data=data)
test.emm <- emmeans(fit, "group")
contest<-emmeans(test.emm, pairwise ~ group)
test.emm.s <- update(contest$contrasts, infer = c(TRUE, TRUE))
return(summary(test.emm.s))
}
dfer<-function(X,Y){
df<-data.frame(X,Y, stringsAsFactors=FALSE)
return(df)
}
cluster<-subset(data, data$measure == "cluster")
clus_vals<-apply(cluster[2:101], FUN=fit_function, MARGIN = 2 ,data=cluster)
indx<-names(clus_vals)
l <- list()
for (i in seq(1, length(clus_vals), by= 1)){
print(paste("start", i))
df <- dfer(indx[i],clus_vals[i])
colnames(df) <- c("IC", "contrast", "estimate","SE","df", "lower.CL", "upper.CL", "t.ratio", "p.value")
l[[i]] <-df
}
## [1] "start 1"
## [1] "start 2"
## [1] "start 3"
## [1] "start 4"
## [1] "start 5"
## [1] "start 6"
## [1] "start 7"
## [1] "start 8"
## [1] "start 9"
## [1] "start 10"
## [1] "start 11"
## [1] "start 12"
## [1] "start 13"
## [1] "start 14"
## [1] "start 15"
## [1] "start 16"
## [1] "start 17"
## [1] "start 18"
## [1] "start 19"
## [1] "start 20"
## [1] "start 21"
## [1] "start 22"
## [1] "start 23"
## [1] "start 24"
## [1] "start 25"
## [1] "start 26"
## [1] "start 27"
## [1] "start 28"
## [1] "start 29"
## [1] "start 30"
## [1] "start 31"
## [1] "start 32"
## [1] "start 33"
## [1] "start 34"
## [1] "start 35"
## [1] "start 36"
## [1] "start 37"
## [1] "start 38"
## [1] "start 39"
## [1] "start 40"
## [1] "start 41"
## [1] "start 42"
## [1] "start 43"
## [1] "start 44"
## [1] "start 45"
## [1] "start 46"
## [1] "start 47"
## [1] "start 48"
## [1] "start 49"
## [1] "start 50"
## [1] "start 51"
## [1] "start 52"
## [1] "start 53"
## [1] "start 54"
## [1] "start 55"
## [1] "start 56"
## [1] "start 57"
## [1] "start 58"
## [1] "start 59"
## [1] "start 60"
## [1] "start 61"
## [1] "start 62"
## [1] "start 63"
## [1] "start 64"
## [1] "start 65"
## [1] "start 66"
## [1] "start 67"
## [1] "start 68"
## [1] "start 69"
## [1] "start 70"
## [1] "start 71"
## [1] "start 72"
## [1] "start 73"
## [1] "start 74"
## [1] "start 75"
## [1] "start 76"
## [1] "start 77"
## [1] "start 78"
## [1] "start 79"
## [1] "start 80"
## [1] "start 81"
## [1] "start 82"
## [1] "start 83"
## [1] "start 84"
## [1] "start 85"
## [1] "start 86"
## [1] "start 87"
## [1] "start 88"
## [1] "start 89"
## [1] "start 90"
## [1] "start 91"
## [1] "start 92"
## [1] "start 93"
## [1] "start 94"
## [1] "start 95"
## [1] "start 96"
## [1] "start 97"
## [1] "start 98"
## [1] "start 99"
## [1] "start 100"
df_cluster<-bind_rows(l)
df_cluster$pFDR<-p.adjust(df_cluster$p.value, method = "BH")
df_cluster[df_cluster$p.value <= 0.05, ]
violin(cluster,cluster$group,cluster$IC_16)
violin(cluster,cluster$group,cluster$IC_23)
centrality<-subset(data, data$measure == "centrality")
cen_vals<-apply(centrality[2:101], FUN=fit_function, MARGIN = 2 ,data=centrality)
indx<-names(cen_vals)
l <- list()
for (i in seq(1, length(cen_vals), by= 1)){
print(paste("start", i))
df <- dfer(indx[i],cen_vals[i])
colnames(df) <- c("IC", "contrast", "estimate","SE","df", "lower.CL", "upper.CL", "t.ratio", "p.value")
l[[i]] <-df
}
## [1] "start 1"
## [1] "start 2"
## [1] "start 3"
## [1] "start 4"
## [1] "start 5"
## [1] "start 6"
## [1] "start 7"
## [1] "start 8"
## [1] "start 9"
## [1] "start 10"
## [1] "start 11"
## [1] "start 12"
## [1] "start 13"
## [1] "start 14"
## [1] "start 15"
## [1] "start 16"
## [1] "start 17"
## [1] "start 18"
## [1] "start 19"
## [1] "start 20"
## [1] "start 21"
## [1] "start 22"
## [1] "start 23"
## [1] "start 24"
## [1] "start 25"
## [1] "start 26"
## [1] "start 27"
## [1] "start 28"
## [1] "start 29"
## [1] "start 30"
## [1] "start 31"
## [1] "start 32"
## [1] "start 33"
## [1] "start 34"
## [1] "start 35"
## [1] "start 36"
## [1] "start 37"
## [1] "start 38"
## [1] "start 39"
## [1] "start 40"
## [1] "start 41"
## [1] "start 42"
## [1] "start 43"
## [1] "start 44"
## [1] "start 45"
## [1] "start 46"
## [1] "start 47"
## [1] "start 48"
## [1] "start 49"
## [1] "start 50"
## [1] "start 51"
## [1] "start 52"
## [1] "start 53"
## [1] "start 54"
## [1] "start 55"
## [1] "start 56"
## [1] "start 57"
## [1] "start 58"
## [1] "start 59"
## [1] "start 60"
## [1] "start 61"
## [1] "start 62"
## [1] "start 63"
## [1] "start 64"
## [1] "start 65"
## [1] "start 66"
## [1] "start 67"
## [1] "start 68"
## [1] "start 69"
## [1] "start 70"
## [1] "start 71"
## [1] "start 72"
## [1] "start 73"
## [1] "start 74"
## [1] "start 75"
## [1] "start 76"
## [1] "start 77"
## [1] "start 78"
## [1] "start 79"
## [1] "start 80"
## [1] "start 81"
## [1] "start 82"
## [1] "start 83"
## [1] "start 84"
## [1] "start 85"
## [1] "start 86"
## [1] "start 87"
## [1] "start 88"
## [1] "start 89"
## [1] "start 90"
## [1] "start 91"
## [1] "start 92"
## [1] "start 93"
## [1] "start 94"
## [1] "start 95"
## [1] "start 96"
## [1] "start 97"
## [1] "start 98"
## [1] "start 99"
## [1] "start 100"
df_centrality<-bind_rows(l)
df_centrality$pFDR<-p.adjust(df_centrality$p.value, method = "BH")
df_centrality[df_centrality$p.value <= 0.05, ]
violin(centrality,centrality$group,centrality$IC_16)
violin(centrality,centrality$group,centrality$IC_11)
PC<-subset(data, data$measure == "PC")
PC_vals<-apply(PC[2:101], FUN=fit_function, MARGIN = 2 ,data=PC)
indx<-names(PC_vals)
l <- list()
for (i in seq(1, length(PC_vals), by= 1)){
# print(paste("start", i))
df <- dfer(indx[i],PC_vals[i])
colnames(df) <- c("IC", "contrast", "estimate","SE","df", "lower.CL", "upper.CL", "t.ratio", "p.value")
l[[i]] <-df
}
df_PC<-bind_rows(l)
df_PC$pFDR<-p.adjust(df_PC$p.value, method = "BH")
df_PC[df_PC$p.value <= 0.05, ]
chisq.test(table(graph_df$group, graph_df$node_type))
## Warning in chisq.test(table(graph_df$group, graph_df$node_type)): Chi-squared
## approximation may be incorrect
##
## Pearson's Chi-squared test
##
## data: table(graph_df$group, graph_df$node_type)
## X-squared = 4.7689, df = 4, p-value = 0.3118
tapply(graph_df$group, graph_df$node_type, summary)
## $connector
## normal overweight obese
## 34 32 37
##
## $kinless
## normal overweight obese
## 0 0 2
##
## $peripheral
## normal overweight obese
## 66 68 61
balloonplot(table(graph_df$group, graph_df$node_type), main ="Node Frequencies", xlab ="", ylab="",
label = TRUE, show.margins = FALSE, colsrt = 90,text.size=1.5 ,dotsize = 5, colmar=5, dotcolor=cbPalette)
no_<-subset(graph_df, graph_df$group == "normal")
pNo<-balloonplot(table(no_$modules, no_$area), main ="Module Frequencies in areas normal", xlab ="", ylab="",
label = TRUE, show.margins = FALSE, colsrt = 90, text.size=1 ,dotsize = 5, colmar=5, dotcolor=cbPalette[1])
balloonplot(table(no_$module, no_$IC), main ="Module Frequencies by IC", xlab ="", ylab="",
label = TRUE, show.margins = FALSE, colsrt = 90, text.size=1 ,dotsize = 5, colmar=5, dotcolor=cbPalette[1])
ov_<-subset(graph_df, graph_df$group == "overweight")
pOv<-balloonplot(table(ov_$modules, ov_$area), main ="Module Frequencies in areas overweight", xlab ="", ylab="",
label = TRUE, show.margins = FALSE, colsrt = 90, text.size=1 ,dotsize = 5, colmar=5, dotcolor=cbPalette[2])
balloonplot(table(ov_$module, ov_$IC), main ="Module Frequencies by IC", xlab ="", ylab="",
label = TRUE, show.margins = FALSE, colsrt = 90, text.size=1 ,dotsize = 5, colmar=5, dotcolor=cbPalette[2])
ob_<-subset(graph_df, graph_df$group == "obese")
pOb<-balloonplot(table(ob_$modules, ob_$area), main ="Module Frequencies in areas obese", xlab ="", ylab="",
label = TRUE, show.margins = FALSE, colsrt = 90, text.size=1 ,dotsize = 5, colmar=5, dotcolor=cbPalette[3])
balloonplot(table(ob_$module, ob_$IC), main ="Module Frequencies by IC", xlab ="", ylab="",
label = TRUE, show.margins = FALSE, colsrt = 90, text.size=1 ,dotsize = 5, colmar=5, dotcolor=cbPalette[3])
Module summaries Average weight 0 - Visual 1 - Fronto-medial parietal and cerebellum and DMN 2 - Amygdala, auditory, hand mouth, and visual 3 - Cerebellum, CO, dorsal attention 4 - Cerebellum 5 - cerebellum, caudate, putamen, thalamus, VDia 6 - brain stem 7 - brain stem, thalamus 8 - brain stem
High weight 0 - Visual 1 - Amygdala, cerebellum, DMN, fronto-parietal 2 - Visual, hand mouth, dorsal attn 3 - Cerebellum, CO, Fronto-parietal 4 - Cerebellum 5 - caudate, putamen, thalamus, VDia 6 - Brain stem 7 - brain stem
Very high weight 0 - Visual 1 - Cerebellum, DMN, fronto-parietal 2 - Auditory, brainstem, CO, DAttn, Visual, hand mouth 3 - Cerebelllum, CO 4 - Amygdala, cerebellum, DMN 5 - Fronto-parietal 6 - Caudate, putamen, thalamus 7 - brainstem 8 - brain stem, VDia
graph_df$modules<-as.factor(graph_df$modules)
# head(graph_df)
mod_function<-function(y,data){
fit<-lm(y~group, data=data)
test.emm <- emmeans(fit, "group")
contest<-emmeans(test.emm, pairwise ~ group)
test.emm.s <- update(contest$contrasts, infer = c(TRUE, TRUE))
return(summary(test.emm.s))
}
mod_function(graph_df$centrality,graph_df)
mod_function(graph_df$clustering,graph_df)
mod_function(graph_df$PC,graph_df)
mod_function(abs(graph_df$zDegree),graph_df)
violin(graph_df, graph_df$group, abs(graph_df$zDegree))
violin(graph_df, graph_df$group, graph_df$centrality)
violin(graph_df, graph_df$group, graph_df$clustering)
violin(graph_df, graph_df$group, graph_df$PC)
# levels(graph_df$area)
graph_df$modules<-as.factor(graph_df$modules)
warp <- transform(graph_df, mods = interaction(group, area))
zD.gls <- gls(zDegree ~ mods, data = warp)
zD.emm <- emmeans(zD.gls, "mods")
zD.fac <- update(zD.emm, levels = list(
group = c("normal", "overweight", "obese"),
area = c("", "Amygdala", "Auditory", "BrainStem", "Caudate", "Cerebellum", "CinguloOperc",
"Default", "DorsalAttn", "FrontoParietal", "MedialParietal", "None", "Putamen",
"Smhand", "Smmouth", "Thalamus", "VentralAttn", "VentralDiencephalon", "Visual")))
contrast(zD.fac, "pairwise", by = "area", adjust = "fdr")
## area = :
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.45599 0.880 243 -0.518 0.7774
## normal - obese 0.24919 0.880 243 0.283 0.7774
## overweight - obese 0.70519 0.880 243 0.801 0.7774
##
## area = Amygdala:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.84578 1.245 243 -0.679 0.7464
## normal - obese 0.03058 1.245 243 0.025 0.9804
## overweight - obese 0.87636 1.245 243 0.704 0.7464
##
## area = Auditory:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.12536 1.245 243 -0.101 0.9199
## normal - obese 0.24051 1.245 243 0.193 0.9199
## overweight - obese 0.36586 1.245 243 0.294 0.9199
##
## area = BrainStem:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.03087 0.440 243 -0.070 0.9442
## normal - obese 1.37744 0.440 243 3.129 0.0030
## overweight - obese 1.40830 0.440 243 3.199 0.0030
##
## area = Caudate:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.48830 0.880 243 0.555 0.5797
## normal - obese -0.77074 0.880 243 -0.875 0.5733
## overweight - obese -1.25904 0.880 243 -1.430 0.4620
##
## area = Cerebellum:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.65542 0.220 243 -2.978 0.0032
## normal - obese 1.05901 0.220 243 4.811 <.0001
## overweight - obese 1.71442 0.220 243 7.789 <.0001
##
## area = CinguloOperc:
## contrast estimate SE df t.ratio p.value
## normal - overweight -1.12981 0.719 243 -1.572 0.3520
## normal - obese -0.58586 0.719 243 -0.815 0.4500
## overweight - obese 0.54395 0.719 243 0.757 0.4500
##
## area = Default:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.68551 0.471 243 -1.457 0.2197
## normal - obese 0.09304 0.471 243 0.198 0.8434
## overweight - obese 0.77855 0.471 243 1.654 0.2197
##
## area = DorsalAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.93319 0.623 243 -1.499 0.2028
## normal - obese -0.99171 0.623 243 -1.593 0.2028
## overweight - obese -0.05852 0.623 243 -0.094 0.9252
##
## area = FrontoParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight -1.05449 0.508 243 -2.075 0.0586
## normal - obese -1.12517 0.508 243 -2.214 0.0586
## overweight - obese -0.07068 0.508 243 -0.139 0.8895
##
## area = MedialParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.40920 1.245 243 -0.329 0.7427
## normal - obese -1.45150 1.245 243 -1.166 0.6050
## overweight - obese -1.04230 1.245 243 -0.837 0.6050
##
## area = None:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.67461 1.245 243 -0.542 0.8827
## normal - obese 0.03058 1.245 243 0.025 0.9804
## overweight - obese 0.70519 1.245 243 0.566 0.8827
##
## area = Putamen:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.48830 0.719 243 0.679 0.4976
## normal - obese -0.77074 0.719 243 -1.072 0.4271
## overweight - obese -1.25904 0.719 243 -1.751 0.2434
##
## area = Smhand:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.12536 0.623 243 -0.201 0.8406
## normal - obese -0.26264 0.623 243 -0.422 0.8406
## overweight - obese -0.13729 0.623 243 -0.221 0.8406
##
## area = Smmouth:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.12536 0.880 243 -0.142 0.8869
## normal - obese 0.24051 0.880 243 0.273 0.8869
## overweight - obese 0.36586 0.880 243 0.416 0.8869
##
## area = Thalamus:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.18147 0.623 243 0.292 0.7709
## normal - obese -0.26511 0.623 243 -0.426 0.7709
## overweight - obese -0.44659 0.623 243 -0.717 0.7709
##
## area = VentralAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight -1.28828 0.880 243 -1.463 0.4341
## normal - obese -0.36761 0.880 243 -0.418 0.6766
## overweight - obese 0.92067 0.880 243 1.046 0.4451
##
## area = VentralDiencephalon:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.02026 0.880 243 -0.023 0.9817
## normal - obese 1.70713 0.880 243 1.939 0.0805
## overweight - obese 1.72739 0.880 243 1.962 0.0805
##
## area = Visual:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.20103 0.321 243 0.625 0.7985
## normal - obese 0.20218 0.321 243 0.629 0.7985
## overweight - obese 0.00115 0.321 243 0.004 0.9971
##
## Degrees-of-freedom method: df.error
## P value adjustment: fdr method for 3 tests
cer_z<-subset(warp, warp$area == "Cerebellum")
violin(cer_z,cer_z$group,cer_z$zDegree)
VD_z<-subset(warp, warp$area == "VentralDiencephalon")
describeBy(VD_z$zDegree, VD_z$group)
##
## Descriptive statistics by group
## group: normal
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2 1.45 0 1.45 1.45 0 1.45 1.45 0 NaN NaN 0
## ------------------------------------------------------------
## group: overweight
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2 1.47 0 1.47 1.47 0 1.47 1.47 0 NaN NaN 0
## ------------------------------------------------------------
## group: obese
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 2 -0.26 0 -0.26 -0.26 0 -0.26 -0.26 0 NaN NaN 0
Cerebellum Ventral Diencephalon
PC.gls <- gls(PC ~ mods, data = warp)
PC.emm <- emmeans(PC.gls, "mods")
PC.fac <- update(PC.emm, levels = list(
group = c("normal", "overweight", "obese"),
area = c("","Amygdala", "Auditory", "BrainStem", "Caudate", "Cerebellum", "CinguloOperc",
"Default", "DorsalAttn", "FrontoParietal", "MedialParietal", "None", "Putamen",
"Smhand", "Smmouth", "Thalamus", "VentralAttn", "VentralDiencephalon", "Visual")))
contrast(PC.fac, "pairwise", by = "area", adjust = "fdr")
## area = :
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.14157 0.1195 243 1.184 0.3561
## normal - obese -0.04981 0.1195 243 -0.417 0.6772
## overweight - obese -0.19138 0.1195 243 -1.601 0.3319
##
## area = Amygdala:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.17106 0.1690 243 -1.012 0.4688
## normal - obese 0.05474 0.1690 243 0.324 0.7463
## overweight - obese 0.22580 0.1690 243 1.336 0.4688
##
## area = Auditory:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.06154 0.1690 243 -0.364 0.7161
## normal - obese -0.30120 0.1690 243 -1.782 0.2280
## overweight - obese -0.23966 0.1690 243 -1.418 0.2363
##
## area = BrainStem:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.00549 0.0598 243 0.092 0.9269
## normal - obese 0.05289 0.0598 243 0.885 0.6427
## overweight - obese 0.04740 0.0598 243 0.793 0.6427
##
## area = Caudate:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.07927 0.1195 243 -0.663 0.8583
## normal - obese -0.05791 0.1195 243 -0.485 0.8583
## overweight - obese 0.02136 0.1195 243 0.179 0.8583
##
## area = Cerebellum:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.04365 0.0299 243 1.461 0.2180
## normal - obese -0.00996 0.0299 243 -0.333 0.7391
## overweight - obese -0.05361 0.0299 243 -1.794 0.2180
##
## area = CinguloOperc:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.07612 0.0976 243 -0.780 0.8183
## normal - obese -0.05367 0.0976 243 -0.550 0.8183
## overweight - obese 0.02244 0.0976 243 0.230 0.8183
##
## area = Default:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.07064 0.0639 243 1.106 0.4744
## normal - obese 0.06416 0.0639 243 1.004 0.4744
## overweight - obese -0.00648 0.0639 243 -0.102 0.9192
##
## area = DorsalAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.13165 0.0845 243 -1.558 0.1809
## normal - obese -0.20977 0.0845 243 -2.482 0.0412
## overweight - obese -0.07813 0.0845 243 -0.924 0.3562
##
## area = FrontoParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.09754 0.0690 243 -1.413 0.1588
## normal - obese -0.20679 0.0690 243 -2.997 0.0090
## overweight - obese -0.10925 0.0690 243 -1.583 0.1588
##
## area = MedialParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.07480 0.1690 243 -0.442 0.8968
## normal - obese -0.09675 0.1690 243 -0.572 0.8968
## overweight - obese -0.02196 0.1690 243 -0.130 0.8968
##
## area = None:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.06554 0.1690 243 -0.388 0.7894
## normal - obese 0.04520 0.1690 243 0.267 0.7894
## overweight - obese 0.11074 0.1690 243 0.655 0.7894
##
## area = Putamen:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.08323 0.0976 243 -0.853 0.7410
## normal - obese -0.03230 0.0976 243 -0.331 0.7410
## overweight - obese 0.05094 0.0976 243 0.522 0.7410
##
## area = Smhand:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.07013 0.0845 243 -0.830 0.4075
## normal - obese -0.26769 0.0845 243 -3.167 0.0052
## overweight - obese -0.19756 0.0845 243 -2.338 0.0303
##
## area = Smmouth:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.01646 0.1195 243 -0.138 0.8906
## normal - obese -0.23472 0.1195 243 -1.964 0.1036
## overweight - obese -0.21826 0.1195 243 -1.826 0.1036
##
## area = Thalamus:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.05673 0.0845 243 -0.671 0.5059
## normal - obese -0.11304 0.0845 243 -1.337 0.5059
## overweight - obese -0.05631 0.0845 243 -0.666 0.5059
##
## area = VentralAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.04643 0.1195 243 0.388 0.9081
## normal - obese 0.01381 0.1195 243 0.116 0.9081
## overweight - obese -0.03262 0.1195 243 -0.273 0.9081
##
## area = VentralDiencephalon:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.01901 0.1195 243 0.159 0.8738
## normal - obese 0.15633 0.1195 243 1.308 0.3776
## overweight - obese 0.13732 0.1195 243 1.149 0.3776
##
## area = Visual:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.08225 0.0436 243 1.885 0.0607
## normal - obese -0.13303 0.0436 243 -3.048 0.0038
## overweight - obese -0.21528 0.0436 243 -4.933 <.0001
##
## Degrees-of-freedom method: df.error
## P value adjustment: fdr method for 3 tests
DMN_PC<-subset(warp, warp$area == "Default")
violin(DMN_PC, DMN_PC$group, DMN_PC$PC)
FP_PPC<-subset(warp, warp$area == "FrontoParietal")
violin(FP_PPC, FP_PPC$group, FP_PPC$PC)
vis_PPC<-subset(warp, warp$area == "Visual")
violin(vis_PPC, vis_PPC$group, vis_PPC$PC)
# head(warp)
clus.gls <- gls(clustering ~ mods, data = warp)
clus.emm <- emmeans(clus.gls, "mods")
clus.fac <- update(clus.emm, levels = list(
group = c("normal", "overweight", "obese"),
area = c("","Amygdala", "Auditory", "BrainStem", "Caudate", "Cerebellum", "CinguloOperc",
"Default", "DorsalAttn", "FrontoParietal", "MedialParietal", "None", "Putamen",
"Smhand", "Smmouth", "Thalamus", "VentralAttn", "VentralDiencephalon", "Visual")))
contrast(clus.fac, "pairwise", by = "area", adjust = "fdr")
## area = :
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.005474 0.0575 243 0.095 0.9920
## normal - obese 0.004899 0.0575 243 0.085 0.9920
## overweight - obese -0.000575 0.0575 243 -0.010 0.9920
##
## area = Amygdala:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.028490 0.0814 243 -0.350 0.8267
## normal - obese -0.046325 0.0814 243 -0.569 0.8267
## overweight - obese -0.017835 0.0814 243 -0.219 0.8267
##
## area = Auditory:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.028521 0.0814 243 0.350 0.8836
## normal - obese 0.016597 0.0814 243 0.204 0.8836
## overweight - obese -0.011924 0.0814 243 -0.147 0.8836
##
## area = BrainStem:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.013192 0.0288 243 0.459 0.8213
## normal - obese 0.019700 0.0288 243 0.685 0.8213
## overweight - obese 0.006507 0.0288 243 0.226 0.8213
##
## area = Caudate:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.025994 0.0575 243 0.452 0.9778
## normal - obese 0.026241 0.0575 243 0.456 0.9778
## overweight - obese 0.000247 0.0575 243 0.004 0.9966
##
## area = Cerebellum:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.009050 0.0144 243 0.629 0.5299
## normal - obese 0.018253 0.0144 243 1.269 0.5299
## overweight - obese 0.009203 0.0144 243 0.640 0.5299
##
## area = CinguloOperc:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.008303 0.0470 243 0.177 0.9401
## normal - obese 0.004767 0.0470 243 0.101 0.9401
## overweight - obese -0.003537 0.0470 243 -0.075 0.9401
##
## area = Default:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.022142 0.0308 243 0.720 0.5875
## normal - obese 0.038847 0.0308 243 1.263 0.5875
## overweight - obese 0.016706 0.0308 243 0.543 0.5875
##
## area = DorsalAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.006997 0.0407 243 0.172 0.9966
## normal - obese 0.006825 0.0407 243 0.168 0.9966
## overweight - obese -0.000172 0.0407 243 -0.004 0.9966
##
## area = FrontoParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.002437 0.0332 243 0.073 0.9416
## normal - obese 0.030411 0.0332 243 0.915 0.6009
## overweight - obese 0.027974 0.0332 243 0.842 0.6009
##
## area = MedialParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.012066 0.0814 243 0.148 0.8822
## normal - obese 0.039216 0.0814 243 0.482 0.8822
## overweight - obese 0.027149 0.0814 243 0.334 0.8822
##
## area = None:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.004149 0.0814 243 -0.051 0.9594
## normal - obese -0.026515 0.0814 243 -0.326 0.9594
## overweight - obese -0.022367 0.0814 243 -0.275 0.9594
##
## area = Putamen:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.025571 0.0470 243 0.544 0.6680
## normal - obese 0.045746 0.0470 243 0.974 0.6680
## overweight - obese 0.020175 0.0470 243 0.429 0.6680
##
## area = Smhand:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.037030 0.0407 243 0.910 0.5455
## normal - obese 0.044031 0.0407 243 1.082 0.5455
## overweight - obese 0.007001 0.0407 243 0.172 0.8635
##
## area = Smmouth:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.027899 0.0575 243 0.485 0.7582
## normal - obese 0.045632 0.0575 243 0.793 0.7582
## overweight - obese 0.017733 0.0575 243 0.308 0.7582
##
## area = Thalamus:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.017180 0.0407 243 0.422 0.9464
## normal - obese 0.019916 0.0407 243 0.489 0.9464
## overweight - obese 0.002736 0.0407 243 0.067 0.9464
##
## area = VentralAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight -0.007346 0.0575 243 -0.128 0.9523
## normal - obese -0.010793 0.0575 243 -0.188 0.9523
## overweight - obese -0.003447 0.0575 243 -0.060 0.9523
##
## area = VentralDiencephalon:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.023993 0.0575 243 0.417 0.9804
## normal - obese 0.022575 0.0575 243 0.392 0.9804
## overweight - obese -0.001417 0.0575 243 -0.025 0.9804
##
## area = Visual:
## contrast estimate SE df t.ratio p.value
## normal - overweight 0.012462 0.0210 243 0.593 0.8674
## normal - obese 0.000767 0.0210 243 0.037 0.9709
## overweight - obese -0.011695 0.0210 243 -0.557 0.8674
##
## Degrees-of-freedom method: df.error
## P value adjustment: fdr method for 3 tests
cent.gls <- gls(centrality ~ mods, data = warp)
cent.emm <- emmeans(cent.gls, "mods")
cent.fac <- update(cent.emm, levels = list(
group = c("normal", "overweight", "obese"),
area = c("","Amygdala", "Auditory", "BrainStem", "Caudate", "Cerebellum", "CinguloOperc",
"Default", "DorsalAttn", "FrontoParietal", "MedialParietal", "None", "Putamen",
"Smhand", "Smmouth", "Thalamus", "VentralAttn", "VentralDiencephalon", "Visual")))
contrast(cent.fac, "pairwise", by = "area", adjust = "fdr")
## area = :
## contrast estimate SE df t.ratio p.value
## normal - overweight -2.21e-04 0.002159 243 -0.103 0.9184
## normal - obese 3.09e-04 0.002159 243 0.143 0.9184
## overweight - obese 5.30e-04 0.002159 243 0.246 0.9184
##
## area = Amygdala:
## contrast estimate SE df t.ratio p.value
## normal - overweight 1.06e-03 0.003053 243 0.348 0.8321
## normal - obese 1.71e-03 0.003053 243 0.561 0.8321
## overweight - obese 6.48e-04 0.003053 243 0.212 0.8321
##
## area = Auditory:
## contrast estimate SE df t.ratio p.value
## normal - overweight -5.20e-04 0.003053 243 -0.170 0.9926
## normal - obese -5.48e-04 0.003053 243 -0.180 0.9926
## overweight - obese -2.84e-05 0.003053 243 -0.009 0.9926
##
## area = BrainStem:
## contrast estimate SE df t.ratio p.value
## normal - overweight 2.75e-05 0.001080 243 0.025 0.9921
## normal - obese 1.07e-05 0.001080 243 0.010 0.9921
## overweight - obese -1.67e-05 0.001080 243 -0.016 0.9921
##
## area = Caudate:
## contrast estimate SE df t.ratio p.value
## normal - overweight -3.74e-04 0.002159 243 -0.173 0.9541
## normal - obese 1.24e-04 0.002159 243 0.058 0.9541
## overweight - obese 4.99e-04 0.002159 243 0.231 0.9541
##
## area = Cerebellum:
## contrast estimate SE df t.ratio p.value
## normal - overweight -1.45e-05 0.000540 243 -0.027 0.9786
## normal - obese -1.40e-04 0.000540 243 -0.259 0.9786
## overweight - obese -1.26e-04 0.000540 243 -0.233 0.9786
##
## area = CinguloOperc:
## contrast estimate SE df t.ratio p.value
## normal - overweight -5.63e-05 0.001763 243 -0.032 0.9746
## normal - obese 3.26e-04 0.001763 243 0.185 0.9746
## overweight - obese 3.82e-04 0.001763 243 0.217 0.9746
##
## area = Default:
## contrast estimate SE df t.ratio p.value
## normal - overweight -4.72e-04 0.001154 243 -0.409 0.8564
## normal - obese -6.81e-04 0.001154 243 -0.590 0.8564
## overweight - obese -2.09e-04 0.001154 243 -0.181 0.8564
##
## area = DorsalAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight -6.86e-05 0.001527 243 -0.045 0.9642
## normal - obese 1.06e-04 0.001527 243 0.069 0.9642
## overweight - obese 1.74e-04 0.001527 243 0.114 0.9642
##
## area = FrontoParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight -3.91e-05 0.001247 243 -0.031 0.9750
## normal - obese -5.01e-04 0.001247 243 -0.402 0.9750
## overweight - obese -4.62e-04 0.001247 243 -0.371 0.9750
##
## area = MedialParietal:
## contrast estimate SE df t.ratio p.value
## normal - overweight 1.18e-04 0.003053 243 0.039 0.9692
## normal - obese -6.35e-04 0.003053 243 -0.208 0.9692
## overweight - obese -7.53e-04 0.003053 243 -0.247 0.9692
##
## area = None:
## contrast estimate SE df t.ratio p.value
## normal - overweight -5.95e-04 0.003053 243 -0.195 0.8456
## normal - obese 9.49e-04 0.003053 243 0.311 0.8456
## overweight - obese 1.54e-03 0.003053 243 0.506 0.8456
##
## area = Putamen:
## contrast estimate SE df t.ratio p.value
## normal - overweight 2.65e-04 0.001763 243 0.150 0.9026
## normal - obese -2.16e-04 0.001763 243 -0.123 0.9026
## overweight - obese -4.81e-04 0.001763 243 -0.273 0.9026
##
## area = Smhand:
## contrast estimate SE df t.ratio p.value
## normal - overweight -5.56e-04 0.001527 243 -0.364 0.8934
## normal - obese -7.60e-04 0.001527 243 -0.498 0.8934
## overweight - obese -2.05e-04 0.001527 243 -0.134 0.8934
##
## area = Smmouth:
## contrast estimate SE df t.ratio p.value
## normal - overweight -2.54e-04 0.002159 243 -0.117 0.9066
## normal - obese -1.00e-03 0.002159 243 -0.463 0.9066
## overweight - obese -7.46e-04 0.002159 243 -0.346 0.9066
##
## area = Thalamus:
## contrast estimate SE df t.ratio p.value
## normal - overweight 2.36e-04 0.001527 243 0.155 0.9909
## normal - obese 2.53e-04 0.001527 243 0.166 0.9909
## overweight - obese 1.74e-05 0.001527 243 0.011 0.9909
##
## area = VentralAttn:
## contrast estimate SE df t.ratio p.value
## normal - overweight 1.11e-03 0.002159 243 0.515 0.8596
## normal - obese -3.82e-04 0.002159 243 -0.177 0.8596
## overweight - obese -1.49e-03 0.002159 243 -0.692 0.8596
##
## area = VentralDiencephalon:
## contrast estimate SE df t.ratio p.value
## normal - overweight -4.67e-04 0.002159 243 -0.216 0.9709
## normal - obese -7.89e-05 0.002159 243 -0.037 0.9709
## overweight - obese 3.88e-04 0.002159 243 0.180 0.9709
##
## area = Visual:
## contrast estimate SE df t.ratio p.value
## normal - overweight 2.07e-05 0.000788 243 0.026 0.9791
## normal - obese 3.85e-04 0.000788 243 0.489 0.9660
## overweight - obese 3.65e-04 0.000788 243 0.463 0.9660
##
## Degrees-of-freedom method: df.error
## P value adjustment: fdr method for 3 tests
Nice function to make a pretty table
library(mosaic)
## Loading required package: lattice
## Warning: package 'lattice' was built under R version 3.6.2
## Loading required package: ggformula
## Loading required package: ggstance
## Warning: package 'ggstance' was built under R version 3.6.2
##
## Attaching package: 'ggstance'
## The following objects are masked from 'package:ggplot2':
##
## geom_errorbarh, GeomErrorbarh
##
## New to ggformula? Try the tutorials:
## learnr::run_tutorial("introduction", package = "ggformula")
## learnr::run_tutorial("refining", package = "ggformula")
## Loading required package: mosaicData
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2
##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.
##
## Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.
##
## Have you tried the ggformula package for your plots?
##
## Attaching package: 'mosaic'
## The following object is masked from 'package:Matrix':
##
## mean
## The following objects are masked from 'package:psych':
##
## logit, rescale
## The following object is masked from 'package:plyr':
##
## count
## The following objects are masked from 'package:dplyr':
##
## count, do, tally
## The following object is masked from 'package:purrr':
##
## cross
## The following object is masked from 'package:ggplot2':
##
## stat
## The following objects are masked from 'package:stats':
##
## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var
## The following objects are masked from 'package:base':
##
## max, mean, min, prod, range, sample, sum
tablr<-function(Y,x,D){
Q<-favstats(Y ~ x, data = D)
Q.stat <- Q[, c("x", "n", "mean", "sd")]
colnames(Q.stat)<-c("test","n", "mean", "sd")
a<-match.call()[2]
return(Q.stat)
}
quickr<-function(X, Y, Z, X2, Y2, Z2, X3){
a<-merge(X, Y, by="test")
b<-merge(a, Z, by="test")
c<-merge(X2, Y2,by="test")
d<-merge(c, Z2, by="test")
# e<-merge(d, X3, by="test")
d$test<-revalue(d$test, c( "no" = "Total",
"ov" = "Total",
"ob" = "Total"))
e<-rbind(b,d)
f<-merge(e,X3, by="test")
drops <- c("Y")
f<-f[ , !(names(f) %in% drops)]
# f<-rbind(e,X3)
library("plyr")
f$test<-revalue(f$test, c("Am. Indian/Alaskan Nat."= "Native American",
"Asian/Nat. Hawaiian/Othr Pacific Is."= "Asian, Native Hawaiian, or Pacific Islander",
"Black or African Am."= "Black or African American",
"More than one"= "More than one race",
"Unknown or Not Reported"= "Unknown or chose not to report",
"White"= "White",
"Total"="Total"))
f$test <- factor(f$test, levels = c( "Native American",
"Asian, Native Hawaiian, or Pacific Islander",
"Black or African American",
"More than one race",
"Unknown or chose not to report",
"White",
"Total"))
return(f)
}
sexr<-function(X,Y){
a <- table(X,Y)
b <- prop.table(a,margin=2)
c<-round(b*100, 1)
d<-data.frame(c)
e<-subset(d, d$Y == "F")
e<-rename(e, c("X"="test"))
A<-table(Y)
B <- prop.table(A)
C<-round(B*100, 1)
D<-data.frame(C)
E<-subset(D, D$Y == "F")
test <- rep("Total",length(1))
G <- cbind(test, E)
f<-rbind(e,G)
# return(rename(f, c("X"="test")))
return(f)
}
“Native American”, “Asian, Native Hawaiian, or Pacific Islander”,“Black or African American”,“More than one race”,“Unknown or chose not to report”,“White”,“Total” X Y Freq
demo_vars<-c("Race","BMI","HbA1C","Age_in_Yrs","group", "Gender")
dems<-dat[demo_vars]
groupSex<-table(dems$group, dems$Gender)
groupSex <- prop.table(groupSex,margin=2)
round(groupSex*100, 1)
##
## F M
## no 52.6 38.4
## ov 24.6 39.0
## ob 22.8 22.7
#dems<-na.omit(dems)
dem_no<-subset(dems, dems$group == "no")
dem_no$group<-factor(dem_no$group)
dem_ov<-subset(dems, dems$group == "ov")
dem_ov$group<-factor(dem_ov$group)
dem_ob<-subset(dems, dems$group == "ob")
dem_ob$group<-factor(dem_ob$group)
## The following `from` values were not present in `x`: ov, ob
## The following `from` values were not present in `x`: no, ob
## The following `from` values were not present in `x`: no, ov
colz<-c("Race/Ethncity",
"n","mean","SD",
"n","mean","SD",
"n","mean","SD",
"%")
digz<-c(0, 2, 2, 0, 2, 2,0, 2, 2)
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
| Race/Ethncity | n | mean | SD | n | mean | SD | n | mean | SD | % |
|---|---|---|---|---|---|---|---|---|---|---|
| Average BMI | ||||||||||
| Native American | 0 | NaN | NA | 0 | NaN | NA | 0 | NaN | NA | 0.0 |
| Asian, Native Hawaiian, or Pacific Islander | 19 | 21.48 | 2 | 12 | 5.16 | 1 | 19 | 26.53 | 4 | 12.2 |
| Black or African American | 23 | 22.73 | 1 | 12 | 5.40 | 0 | 23 | 28.22 | 4 | 16.7 |
| More than one race | 6 | 22.63 | 2 | 6 | 5.30 | 0 | 6 | 25.67 | 3 | 5.6 |
| Unknown or chose not to report | 0 | NaN | NA | 0 | NaN | NA | 0 | NaN | NA | 0.0 |
| White | 108 | 22.18 | 2 | 73 | 5.18 | 0 | 108 | 28.81 | 4 | 65.6 |
| Total | 156 | 22.20 | 2 | 103 | 5.21 | 0 | 156 | 28.33 | 4 | 57.7 |
| High BMI | ||||||||||
| Native American | 1 | 29.23 | NA | 1 | 5.90 | NA | 1 | 35.00 | NA | 2.4 |
| Asian, Native Hawaiian, or Pacific Islander | 6 | 25.86 | 1 | 2 | 5.00 | 0 | 6 | 25.33 | 5 | 7.1 |
| Black or African American | 17 | 27.93 | 2 | 12 | 5.41 | 0 | 17 | 28.53 | 3 | 21.4 |
| More than one race | 4 | 26.31 | 1 | 2 | 5.20 | 0 | 4 | 22.75 | 0 | 4.8 |
| Unknown or chose not to report | 2 | 27.69 | 2 | 2 | 5.25 | 0 | 2 | 29.00 | 4 | 0.0 |
| White | 79 | 27.16 | 1 | 57 | 5.19 | 0 | 79 | 28.51 | 3 | 64.3 |
| Total | 109 | 27.20 | 2 | 76 | 5.23 | 0 | 109 | 28.19 | 4 | 38.5 |
| Very high BMI | ||||||||||
| Native American | 0 | NaN | NA | 0 | NaN | NA | 0 | NaN | NA | 0.0 |
| Asian, Native Hawaiian, or Pacific Islander | 0 | NaN | NA | 0 | NaN | NA | 0 | NaN | NA | 0.0 |
| Black or African American | 14 | 34.73 | 5 | 7 | 6.19 | 2 | 14 | 30.57 | 4 | 25.6 |
| More than one race | 2 | 32.05 | 2 | 2 | 5.70 | 0 | 2 | 30.00 | 0 | 5.1 |
| Unknown or chose not to report | 4 | 32.53 | 3 | 4 | 5.10 | 0 | 4 | 26.00 | 2 | 5.1 |
| White | 58 | 34.30 | 3 | 35 | 5.25 | 0 | 58 | 29.26 | 4 | 64.1 |
| Total | 78 | 34.23 | 4 | 48 | 5.39 | 1 | 78 | 29.35 | 4 | 50.0 |